app.py

import streamlit as st
from transformers import AutoTokenizer, AutoModel
import torch

# Load the model and tokenizer
@st.cache(allow_output_mutation=True)
def load_model():
    tokenizer = AutoTokenizer.from_pretrained("Salesforce/SFR-Embedding-Mistral")
    model = AutoModel.from_pretrained("Salesforce/SFR-Embedding-Mistral")
    return tokenizer, model

tokenizer, model = load_model()

def embed_text(text):
    inputs = tokenizer(text, return_tensors='pt', truncation=True, max_length=32768)
    outputs = model(**inputs)
    return outputs.last_hidden_state.mean(dim=1).detach().numpy()

def main():
    st.title("Text Embedding using Salesforce/SFR-Embedding-Mistral")

    # Text input
    text = st.text_area("Enter text here:", height=150)
    
    if st.button("Get Embeddings"):
        if text:
            with st.spinner('Fetching embeddings...'):
                embeddings = embed_text(text)
                st.write(embeddings)
        else:
            st.warning("Please enter some text to process.")

if __name__ == "__main__":
    main()